Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, USA.
Interdisciplinary Center for Network Science and Applications, University of Notre Dame, Notre Dame, IN, USA.
PLoS Comput Biol. 2020 Sep 18;16(9):e1008192. doi: 10.1371/journal.pcbi.1008192. eCollection 2020 Sep.
Balanced excitation and inhibition is widely observed in cortex. How does this balance shape neural computations and stimulus representations? This question is often studied using computational models of neuronal networks in a dynamically balanced state. But balanced network models predict a linear relationship between stimuli and population responses. So how do cortical circuits implement nonlinear representations and computations? We show that every balanced network architecture admits stimuli that break the balanced state and these breaks in balance push the network into a "semi-balanced state" characterized by excess inhibition to some neurons, but an absence of excess excitation. The semi-balanced state produces nonlinear stimulus representations and nonlinear computations, is unavoidable in networks driven by multiple stimuli, is consistent with cortical recordings, and has a direct mathematical relationship to artificial neural networks.
皮层中广泛存在着兴奋和抑制的平衡。这种平衡如何塑造神经计算和刺激表示？这个问题通常使用处于动态平衡状态的神经元网络的计算模型来研究。但是，平衡网络模型预测刺激和群体反应之间存在线性关系。那么，皮质电路如何实现非线性表示和计算？我们表明，每个平衡网络结构都允许存在打破平衡状态的刺激，这些平衡的破坏将网络推向一个以某些神经元过度抑制为特征的“半平衡状态”，但不存在过度兴奋。半平衡状态产生非线性刺激表示和非线性计算，在受多种刺激驱动的网络中是不可避免的，与皮层记录一致，并且与人工神经网络有直接的数学关系。
